Psychological and psychosocial determinants of COVID‐related handwashing behaviours: A systematic review

Abstract Background The COVID‐19 pandemic, caused by the SARS‐CoV‐2 virus, has resulted in illness, deaths and societal disruption on a global scale. Societies have implemented various control measures to reduce transmission of the virus and mitigate its impact. Individual behavioural changes are crucial to the successful implementation of these measures. One commonly recommended measure to limit risk of infection is frequent handwashing. It is important to identify those factors that can predict the uptake and maintenance of handwashing. Objectives We aimed to identify and synthesise the evidence on malleable psychological and psychosocial factors that determine uptake and adherence to handwashing aimed at reducing the risk of infection or transmission of COVID‐19. Search Methods We searched various literature sources including electronic databases (Medline ALL, Child Development & Adolescent Studies, ERIC, PsycInfo, CINAHL and Web of Science), web searches, conference proceedings, government reports, other repositories of literature and grey literature. The search strategy was built around three concepts of interest including (1) context (terms relating to COVID‐19), (2) behaviour of interest and (3) terms related to psychological and psychosocial determinants of COVID Health‐Related Behaviours and adherence or compliance with handwashing, to capture malleable determines. Searches capture studies up until October 2021. Selection Criteria Eligibility criteria included observational studies (both retrospective and prospective) and experimental studies that measure and report malleable psychological and psychosocial determinants and handwashing at an individual level, amongst the general public. Screening was supported by the Cochrane Crowd. Titles and abstracts were screened against the eligibility criteria by three independent screeners. Following this, all potentially relevant studies were screened at full‐text level by the research team. All conflicts between screeners were resolved by discussion between the core research team. Data Collection and Analysis All data extraction was managed in EPPI‐Reviewer software. All eligible studies, identified through full‐text screening were extracted by one author. We extracted data on study information, population, determinant, behaviour and effects. A second author checked data extraction on 20% of all included papers. All conflicts were discussed by the two authors until consensus was reached. We assessed methodological quality of all included studies using an adapted version of the Joanna Briggs Institute Quality appraisal tool for cross‐sectional studies. Main Results Our initial searches yielded 23,587 results, of which 56 studies were included in this review. The included studies were cross sectional in design, came from 22 countries and had a combined sample of 199,376 participants. The vast majority of studies had samples from the general public, with eight of the studies focusing on specific samples. All included studies considered people over the age of 18. The quality of the majority of the studies was good (n = 30 rated low risk of bias), with 8 rated high risk of bias, predominately due to lack of reporting of recruitment, sample characteristics and methodology. Thirty‐four studies were included in the narrative synthesis and 28 in the meta‐analysis. Findings indicated that emotions about COVID‐19 (worry [0.381, confidence interval [CI] = 0.270–0.482, I 2 = 92%) and anxiety (0.308, CI = 0.154–0.448, I 2 = 91%]), knowledge of COVID‐19 (0.323, CI = 0.223–0.417, I 2 = 94%), and perceived social norms (0.303, CI = 0.184–0.413, I 2 = 92%) were among the malleable determinants most associated with handwashing. Perceived severity (0.006, CI = ‐0.011–0.023) and susceptibility of COVID‐19 (0.041, CI = −0.034 to 0.115) had little to no effect on handwashing behaviour. Authors' Conclusions Understanding the effects of various malleable determinants on COVID‐related handwashing can aid in the development and implementation of interventions and public health campaigns to promote handwashing behaviour in potential new waves of COVID‐19 or other respiratory infections. Emotions about COVID, knowledge of COVID and perceived social norms warrant further consideration in future research and policy.


| The problem, condition or issue
Severe acute respiratory coronavirus 2 (SARS-CoV-2) emerged in late 2019 and spread rapidly around the globe (Cucinotta & Vanelli, 2020;Wu et al., 2020).The pandemic of COVID-19 disease, caused by SARS-CoV-2, has resulted in short and long-term illness, deaths and societal disruption.Societies implemented control measures to reduce the transmission of the virus.Individual behaviour change is crucial to the success of these measures through reducing the frequency of social contacts, mitigating the risk of those social contacts and reducing the amount of time that infectious people are in contact with others whom they may infect.Despite vaccine programmes being introduced in December 2020, waning immunity and the evolution of new variants, indicate the significance of behavioural measures to reduce the spread (Girum et al., 2021;Michie & West, 2020).
The behaviours to reduce the risk of catching or spreading SARS-CoV-2 including: handwashing or use of hand sanitiser, wearing masks or face coverings, physical distancing, social distancing, isolation or quarantine, respiratory hygiene, cleaning surfaces, avoiding touching the 'T-zone' (mouth, nose and eyes) (Elder et al., 2014) as well as other composite measures that include these behaviours.
The evidence for the effectiveness of these measures has been established during previous pandemics of similar serious viral respiratory infections such as pandemic Influenza A (H1N1), SARS and MERS (Flumignan et al., 2020;Jefferson et al., 2020;Seto et al., 2003;Warren-Gash et al., 2013;Webster et al., 2020;West et al., 2020).It is important to synthesise the evidence on the determinants of these measures during the COVID-19 pandemic, that may be applied to future pandemics of influenza and other serious respiratory infectious diseases.

| Exposure/determinants
The exposure in this review was psychological or psychosocial determinants of handwashing.To be included, determinants were malleable factors that could, theoretically, be changed by a public health intervention.

| Why it is important to do this review
Handwashing cannot be effective on a societal level if it is not adopted widely and consistently.Variables such as individual health beliefs, social support, culture, and social norms can all influence the likelihood of someone undertaking and maintaining health behaviours such as handwashing.To develop appropriate public health interventions to improve uptake and adherence to handwashing, including effective messaging, it is important to understand the malleable factors that influence this behaviour.We identified and examined all existing research evidence that described a relationship between any malleable factor or determinant (or those that can be most effectively targeted as part of public health interventions) and handwashing in the context of SARS-CoV-2.
In this review, we are interested in the evidence on malleable and non-malleable psychological and psychosocial factors associated with uptake and adherence to health protective behaviours.
Malleable determinants in this EGM refer to psychological and psychosocial factors that can be developed, shaped or altered.
Factors such knowledge, access to information, emotions, and perceptions.Non-malleable determinants in this EGM refer to factors or attributes that are fixed or unchangeable through public health intervention.Factors such as age, sex, income, past behaviour, and health status.
In any future severe viral outbreaks, health-protective behaviours, such as handwashing, will be vital to reducing risk of infection and transmission.Non-pharmaceutical interventions that are designed to improve the uptake and adherence to protective behaviours are essential in an outbreak, and in particular when

| Overview of the COHeRe project
COHeRe is a UKRI funded project https://www.qub.ac.uk/schools/ psy/Research/OurResearchThemes/HealthWelfareClinicalPsychology/ COHeRe/ made up of a team with substantial expertise in systematic reviews, health behaviour and infectious diseases.The overall aim of the project was to identify, synthesis, and examine evidence on determinates of COVID-19 health-related behaviours.The specific behaviours of interest were as follows: • Handwashing • Wearing masks/face coverings

• Physical Distancing
• Social Distancing • Isolation/quarantine • Respiratory hygiene • Cleaning surfaces • Avoiding t-zone • Other composite measures that include the above.
During Phase 1 of the project a rapid review was conducted, which examined determinants of protective behaviours during COVID-19 and during previous outbreaks of similar serious respiratory infections, for example, SARS, MERS and H1N1 (swine flu) (Hanratty et al., 2021).Of the 233 studies included in the rapid review, 54 were conducted in the context of COVID-19, while the remainder were conducted in the context of other respiratory infections.Over the course of conducting the rapid review, it became apparent that the evidence base examining determinants in the context of COVID-19 was rapidly expanding and further identification and examination was needed of this new evidence.
On this basis, further funding was secured to conduct Phase 2 of the project, which identified and mapped the existing evidence (published and unpublished between January 2020 and October 2021) on malleable and non-malleable psychological and psychosocial factors that determine uptake and adherence to behaviours aimed at reducing the risk of infection or transmission of COVID-19 (Hanratty et al., 2022(Hanratty et al., , 2023))

| OBJECTIVES
We intended to identify and synthesise the existing evidence on malleable psychological and psychosocial factors that determine uptake and adherence to handwashing that can reduce the risk of infection or transmission of COVID-19.

| METHODS
None 5.1 | Criteria for considering studies for this review

| Types of studies
This systematic review contains studies that quantify the relationship between a malleable determinant and handwashing.
Included study designs consisted of observational studies (both retrospective and prospective) and experimental studies that measure and report malleable psychological and psychosocial determinants and handwashing at an individual level.We did not include narrative reviews, modelling studies, letters, editorials, opinion pieces, news, commentaries, or any other publications that did not report primary data.

| Types of participants
The population of interest is members of the general public, of any age.Within the group of studies of the general public, we included studies on specific groups of people that may be at increased risk of catching the virus for example, people who work in essential retail services.Similarly, we included studies of specific patient groups at increased risk of becoming seriously ill if infected, for example, those with existing chronic respiratory disorders.However, we did not include studies on health care workers (HCWs), defined as someone who works in a hospital or health care setting or delivers health care in the community.This population typically have, or should have additional knowledge, training and resources to support the adoption of behaviours to mitigate against the increased risk of exposure to infectious diseases.A rapid review on barriers and facilitators to HCWs adherence to infection prevention and control guidelines has been published (Houghton et al., 2020).For those studies that included both HCWs and the public, were only included if data on the public is presented separately from data on healthcare workers.

| Exposure/determinants
The exposure in this review was psychological or psychosocial determinants of handwashing.To be included determinants were malleable factors that could, theoretically, be changed by a public health intervention.
We developed 10 categories of determinants for phase 2 of this project.Information included seeking and consuming information, the quality or source of information, and determinants related to public health messaging, for example, message content or framing.
Other was the final category of determinants and includes any determinants that did not fit within the previous broad categories.
This was divided into subcategories of beliefs, for example, political beliefs, social (e.g., social capital, social networks), practical resources such as access to masks, paid sick leave, time included time since the outbreak began, cultural determinants such as collectivist vs individualist cultures, and a final 'other' subcategory for any remaining determinant that did not fit into the previous subcategories.
The determinants of behaviour, demographics, disease, and health status were not included as these were categorised as nonmalleable.We also did not include studies that examined interventions as a determinant of handwashing as this will be analysed in a separate review.
Comparators were the absence of the determinant (compared to its presence) or, where a determinant is presented as a continuous measure, then analysis will be based on correlation between handwashing and determinants.
We included studies that measured determinants at an individual level and group level, for example, country-level data on the number of cases.
We included studies on self-reported or observed determinants.
Self-reports included actual or perceived determinants, for example 'risk of contracting the virus' could be measured by quantifying actual risk based on individual circumstances and behaviour or through selfreported perceived risk.

| Types of outcome measures
While our searches sought to identify evidence on commonly recommended behaviours to mitigate human-to-human spread of COVID-19 as described by (West et al., 2020), this current review focuses on handwashing only.We define handwashing as, washing hands more frequently with soap and water or the use of hand sanitizer if handwashing facilities are not available.
We included studies on actual handwashing behaviour, through self/other report and/or observation, measured at the individual level.We excluded studies that measured intended behaviour or hypothetical behaviour.

Primary outcomes
The primary outcome of this review was handwashing.No secondary outcome was considered.

| Search methods for identification of studies
To ensure that the literature contained in the review was relevant and useful to key stakeholders, it was important that the literature retrieval methods followed high-quality standards and all searches were conducted and reported following Campbell Collaboration guidelines (White et al., 2020).
Information retrieval specialist author (CK) developed and piloted a search strategy with input from clinical and behaviour change expert authors (DB and MD).This strategy was further refined by CK following expert advice from a Campbell information retrieval specialist during the editorial/peer review of the protocol.Searches strageries in the current review capture studies up until October 2021.
The search strategy was built around three concepts of interest; (1) Context (terms relating to COVID-19).For concept one, we used an innovative and tested COVID-19 search strategy was developed for use by NICE information specialists and was updated as recently as 21 June 2021 (Levay & Finnegan, 2021).An example of the search string was piloted in Medline (Ovid) and is presented in Table 3.
(3) Terms related to psychological and psychosocial determinants of For concept 2 and 3 the terms used were based on those used in the rapid review (Hanratty et al., 2021) which itself was informed through consultation with the Behaviour Change Group formed in response to COVID-19 by the Public Health Agency, Northern Ireland.The terms were then piloted and refined in two databases, with unique terms added and redundant or duplicate terms removed (Table 2).

| Electronic databases
Based on the Queens's University Belfast database subscriptions, we searched the following key information sources to locate relevant primary research: • Medline ALL (Ovid) • ERIC (EBSCOhost) • PsycInfo 1806-present (OVID) • CINAHL Plus (EBSCOhost) • Web of Science Core Collection (the QUB subscription includes SCI-expanded, SSCI, A&HCI, CPCI-S, CPCI-SSH, ESHI) To locate relevant secondary research for inclusion in the EGM, we searched the following information resources: • The Social Care Institute for Excellence (SCIE) • The Cochrane Library (Psychological|Psychosocial)(behavior|behaviour) we will limit returns by 'Since 2020' filter and sort remaining records by relevance.We downloaded the first 1000 articles (which is the upper limit set by Google) using Harzing's Publish or Perish software.
• clinicaltrials.gov • ISRCTN Registry (https://www.isrctn.com/) • WHO International Clinical Trials Registry Platform (ICTRP) (https://www.who.int/clinical-trials-registry-platform/the-ictrpsearch-portal) And by contacting and reviewing the information of the following key organisations in the UK with proven experience on the topics related to this project: • King's Fund (https://www.kingsfund.org.uk/) • National Institute for Health Research (https://www.nihr.ac.uk/) • NHS Evidence (https://www.evidence.nhs.uk/) We considered searching ProQuest dissertations and theses, however, we assessed that it was unlikely that any relevant doctoral theses would be complete and available in the timeframe of the virus.
We conducted a search of reference lists of previous reviews and eligible articles to identify any additional studies not identified through the electronic search.Finally, when we compiled a list of included studies, we contacted key experts in the field via email (categorised as 'key' if they have published five or more included studies) to ask whether they were aware of any unpublished or ongoing research that might not have been easily accessible to the research team.
To locate additional relevant grey literature for inclusion in the EGM, we searched for ongoing or unpublished reviews via: • PROSPERO, • Figshare and the • Open Science Framework (OSF).
Any ongoing reviews were checked again before completion of the project, and, if still unpublished were excluded from the map.

| Search limits
Due to the limited language skills of the review team, we only included studies published in English.
We limited our search to exclude opinion pieces, letters, editorials and unpublished reports in databases where these limits are supported (See Table 3: lines 7 and 35).We did not use database limiters for studies on humans only as we found these limiters excluded a substantial number of potentially relevant papers not indexed as 'human' studies.Instead, we have opted to use an adaptation of the Cochrane search filter for human studies (line 7 and 35).
We included only those studies which were conducted during the ongoing COVID-19 pandemic.We included studies from Jan 2020 until the date of the final search.

| Selection of studies
All search results were first screened on titles and abstracts against the eligibility criteria by three independent screeners.Screening at this first stage was supported by the Cochrane Crowd.We retrieved a full-text copy of all potentially relevant studies during the title and abstract screening.Following this, all potentially relevant studies were screened independently by at least two reviewers from the research team at full-text level.All conflicts between screeners were resolved by discussion between the core research team.

| Data extraction and management
All data extraction was managed in EPPI-Reviewer software.All eligible studies, identified through full-text screening were extracted T A B L E 3 Demographics of included studies.Extracted information included (Supporting Information 5):

Study
• Study information: Author, year, country, study design, when the study was conducted, sample size.
• Population: description of the population, age, sex.
• Exposure: determinant measured, description of the determinant, who measured the determinant, type of measurement (observation, self-reported, etc.), direction and quality of the scale.
• Outcome: behaviour measured, description of the behaviour, who measured the behaviour, type of measurement (observation, selfreported, etc.), direction and quality of the scale.
• Effects: Narrative description of the finding, effect size information or sufficient numerical data to allow us to calculate the effect size.

| Quality appraisal
The JBI tool for cross-sectional studies was used to assess the quality of included studies (The Joanna Briggs Institute, 2017; The Joanna Briggs Institute, 2020).After piloting the JBI tool on some known studies we decided to modify the tool to ensure that they are fit for our purposes (Supporting Information 6).We changed the wording of the second item 'were the study subjects and the setting described in detail' to 'was the sample included in the study representative of the population of interest?' to assess whether or not the sample was representative of the population of interest.We also changed the wording slightly, replacing condition and exposure with behaviours of interest and determinants, respectively.
The eight questions were answered with either 'yes', 'no', or 'unclear'.For the questions on scale validity and reliability, we indicated whether a single-item or multiple-item scale was used and whether or not this was reliable and valid.Each study was rated either low, high or unclear risk of bias through adding up the total number of items answered 'yes'.For example, >70% yes = Low Risk of Bias, 50%-70% yes = Unclear Risk of Bias, and <50% 'Yes' = High Risk of Bias.

| Measures of treatment effect
We extracted data on the relationship between handwashing and determinants of that behaviour.Outcomes were reported T A B L E 3 (Continued) | 13 of 37

Study
in both dichotomous and continuous data.The meta-analysis was performed using Comprehensive Meta-Analysis Version 4 (Comprehensive Meta-Analysis Version 4, 2022), and conducted using correlation coefficients (r), as that was the effect size statistic most commonly reported in the papers.Therefore, data was extracted that allowed us to convert or calculate r.For example, where summary statistics were not presented, we extracted data such as means and standard deviations that allowed us to calculate a standardised mean difference that was then converted to r.Effect sizes were interpreted according to thresholds suggested by Cohen 1988: weak (r = 0.1), moderate (r = 0.3), and strong (r = 0.5).

| Unit of analysis issues
There are two reports that include multiple studies (Rui et al., 2021;Kowalski 2020a).Given that these separate studies utilised different samples, we treated as individual studies.Each individual study is referred to Author study 1, Author Study 2 and so on.

| Assessment of heterogeneity
Heterogeneity was assessed first, through visual inspection of the forest plot and checking for overlap of confidence intervals and second through the Q, I 2 and τ 2 statistic.Investigation of the source of heterogeneity is addressed in data synthesis section.

| Data synthesis
Given the diverse range of behaviour and determinant relationship examined across the included studies, we used random effects models, using inverse-variance estimation.We conducted separate meta-analyses for each determinant of the behaviour of interest, handwashing.
-Determinants were grouped based on previous mapping (Hanratty et al., 2023); -Determinant groups were included in the meta-analysis if they included data that was suitable for meta-analysis (i.e.unadjusted data) and there was a minimum of three data points; -We excluded adjusted estimates from meta-analyses as there is considerable variation in the covariates used to adjust these estimates across studies and, therefore, we judged that the adjusted estimates were not suitable for statistical aggregation; -Data that was not suitable was synthesised narratively.

Treatment of qualitative research
The review does not include qualitative research.

| Results of the search
As seen in Figure 1, our searches yielded a total of 23,587 results.
After screening out titles/abstracts we were left with 2444 results.
There was a total of 199,376 participants across the 56 studies, ranging from 71,851 (Hsing 2021(Hsing Julianna et al., 2021) to 212 (Pal et al. 2020(Pal et al., 2020).The vast majority of studies had samples from the general public, with eight of the studies focusing on specific samples.These included; pregnant women (Wang 2021 Following assessment of the data, 28 studies were deemed suitable to include in the meta-analysis.These 28 studies reported on 12 determinants.A total of 34 studies were included in the narrative synthesis, reporting six determinants.Studies were considered not suitable for meta-analysis due to not reporting unadjusted data.
Given the multiple determinants reported in individual studies, 6 studies were included in both the narrative synthesis and meta-

| Excluded studies
A total of 87 studies were excluded from this review, a list of which can found in the references.

| Risk of bias in included studies
A detailed summary of risk of bias for the 56 included studies is shown in Table 4.All 56 studies were utilised a cross sectional design and were rated using the JBI tool for cross-sectional studies (The Joanna Briggs Institute, 2017, 2020).Studies were scored based on the number of items answered 'yes', with >70% yes = Low Risk of Bias, 50%-70% yes = Unclear Risk of Bias, and <50% 'Yes' = High Risk of Bias.
Overall, 30 studies were rated low risk of bias, 18 unclear risk of bias, and 8 were rated as high risk bias.Those studies deemed high risk of risk predominately received this rating due to lack of detail on measurement of handwashing and determinants (Bruine 2020; Kebede 2020) or the measure used was deemed not to be a reliable or valid measure (Sengeh 2020(Sengeh et al., 2020).There was also poor reporting of study design and methodology (Graupensperger 2021;    6.3 | Data and analysis

| Meta-analysis
In total we analysed 52 effect sizes across 6 determinant groups, and included 28 studies.The summary effect of each determinant group can be seen in the Summary of findings table 1 along with 95% confidence intervals (CIs) and heterogeneity statistics.As shown in the summary of findings table, our analyses indicate significant relationships between knowledge about behaviour and disease, social norms, COVID-related worry and anxiety and handwashing behaviour.There is no significant relationship observed between perceived severity and handwashing or between perceived susceptibility and handwashing.All data is reported in Tables 5-7.
Below we present forest plots (Figures 2-13) for each determinant and interpret these findings further.
Heterogeneity was significant across all these determinants.For the meta-analysis of attitudes, Norman (2020) provided two effect sizes: one for experiential attitudes (r = 0.44) and one for instrumental attitudes (r = 0.30).We used an average of these two estimates in the meta-analysis.For the meta-analysis of perceived effectiveness, Al-Shammary (2021) provided four effect sizes for the relationship between handwashing and perceived effectiveness of preventive measures in the marketplace (r = 0.26), in the workplace (r = 0.17), in healthcare settings (r = 0.10), and in travel settings (r = 0.16).We used the average of these estimates in the meta-analysis.In the case of social norms, Graupensperger (2021) provided correlations between social norms and handwashing with soap and water (r = 0.49) and also using hand sanitiser (r = 0.47).We used the average of these two correlations in the meta-analysis.
Both perceived severity (13,098 participants) and susceptibility (14,050 participants) had a non-significant correlation with handwashing T A B L E 5 Handwashing and anxiety and worry.For knowledge of disease, a moderate average correlation was found with handwashing across the studies (r = 0.337, 95% CI = 0.238, 0.428, p ≤ 0.001) (Figure 13).Apanga (2021) provided correlations between handwashing and knowledge of: COVID symptoms (r = 0.29), transmission via respiratory droplets (r = 0.09), and transmission via touching contaminated surfaces (r = 0.45).We used the average correlation from this study in the meta-analysis.
There was significant heterogeneity for both knowledge of behaviour F I G U R E 4 Relationship between handwashing and percieved control.CI, confidence interval.

| Narrative synthesis of results
A total of 34 studies were included in the narrative synthesis.Details of the individual studies that contribute to this synthesis are show in Table 3. F I G U R E 7 Relationship between handwashing and perceieved effectiveness.CI, confidence interval.

Barriers influencing handwashing behaviour
counties and found generally weak associations between barriers and using either hand sanitiser or hand soap.In participants living in one of these locations (Hong Kong), a larger effect was observed.
However, it should be noted that the sample size in this location was much smaller (around 1200 participants) in comparison to the other countries where participants were recruited from the USA, Mexico and Taiwan, which had sample sizes ranging from approximately 3000 to 640,000).Weak associations were also found in the studies of Dwipayanti 2021 and Li 2021.

COVID-19-related fear or worry
Weak, but positive associations were reported in three studies | 25 of 37 (Shook Natalie et al., 2020); Nelson 2021 (Nelson Tracy et al., 2021), but again, the reported effect sizes for these associations were weak (Table 10).One of these studies (Nelson 2021) did not report any While the majority of these reported weak effect sizes, the direction of these effects varied, with both positive and negative associations being observed (Table 11).Two studies (Dwipayanti 2021; Mousavi et al., 2022) which recruited participants in Indonesia and Afghanistan, respectively, did report larger associations between variables.One of these (Mousavi et al., 2022), differed from other studies in that it examined the likelihood of family members getting infected, and included a relatively small sample size.In comparison to other evidence included in the analysis, both of these studies had larger confidence intervals around the reported odds ratios.

Other Beliefs and motivations about COVID-19
Ten studies (Al-Shammary 2021;  13).Like the relationships between handwashing and susceptibility, or severity of COVID-19, the effect sizes found here were weak.Studies in this section did suggest that handwashing was more likely when people held beliefs that COVID-19 should be taken more seriously.The strongest associations were found for motivations that were around protecting others.For example, it was observed that while self-protection was a predictor of handwashing, protecting family members and the general public, was more strongly associated with these behaviours (Stojanovic 2021; van den Broek-Altenburg).
T A B L E 8 Handwashing and perceived barriers.

| Summary of main results
This systematic review aimed to synthesise the evidence examining psychosocial factors that determine the uptake and adherence to handwashing and hand sanitising behaviours for reducing the risk of infection or transmission of severe acute respiratory coronavirus 2 (SARS-CoV-2) in the general public.
The review forms part of the CoHeRe project (Hanratty et al., 2022).This interdisciplinary, multinational project has involved the development of an Evidence and Gap Map to identify and summarise current research on determinants of COVID-19 protective behaviours, and a series of individual reviews examining the determinants of these specific behaviours (Hanratty et al., 2022).
This review provides one of the first studies to synthesis, using meta-analyses and narrative summaries, evidence on the malleable Overall findings based on the results of the meta-analysis indicated that emotions about COVID, knowledge of COVID-19, and perceived social norms regarding behaviours were among the malleable determinants most associated with handwashing.Perceived effectiveness, attitudes towards behaviours, and self-efficacy were also linked with these behaviours, albeit with a smaller effect.
Perceived severity and susceptibility of COVID-19 were not associated with handwashing behaviour.
Findings from the meta-analysis and narrative synthesis did therefore show some agreement, particularly related to the association between handwashing behaviours and people's emotions around COVID-19.
It is important to note that the meta-analyses presented in this review have a high degree of heterogeneity (apart from the two meta-analyses that found no significant association between handwashing and perceived severity and susceptibility).This heterogeneity could be a result of variation in the measurement or operational definition of the determinants, or variation in the measurement or operational definition of handwashing, or variation in the timing of the study in relation to government-led initiatives or mandates within each country.Furthermore, the evidence presented in the review is drawn from cross-sectional studies, which prevents any conclusions being drawn that go beyond associations between variables.In other words, the review does not help us to understand how change in the determinants might be related to change in handwashing behaviour.This is a gap for further research.

| Overall completeness and applicability of evidence
To the best of our knowledge, the evidence presented in this review represents the entirety of research to date (completed searches determining determinants of interest.They found that perceived susceptibility and severity was not a significant determinant of hand hygiene, while knowledge, perceived norms, and self-efficacy was significant.Our findings concur and add to those found by (Liang et al., 2022).

| Implications for research
The volume of research on COVID has rapidly increased from the beginning of the pandemic, and continues to emerge.Increased demand to understand the determinants of COVID-19 related behaviour has resulted studies being completed rapidly, often at the expense of the quality of the research (Park et al., 2021).Other studies have similarly pointed to the need for well-designed, good quality studies (Park et al., 2021), on the determinants of COVID related behaviour.In addition, the majority of our studies were from high-income countries, largely the USA and China.COVID-19 is a global pandemic, thus we need to understand how and if the determinants of behaviour vary globally.Finally, the most commonly reported determinants were perceived susceptibility and severity.
Our research has shown these to have little to no effect on handwashing, albeit these results must be interpreted cautiously.
Determinants such as emotions relating to COVID, knowledge about COVID and social norms were less commonly reported; however had a larger effect on handwashing behaviour.These determinants should be considered further.
The Crowd screened over 25,000 records for the project and we are indebted to each individual who contributed to this effort and to Anna-Noel Storr at Cochrane for coordinating.
We also wish to acknowledge the contribution of our advisory groups.Our expert advisory group provided insights and guidance from their collective expertise in COVID-related policy-making, behaviour change and evidence synthesis methodology.Our citizen advisors have generously shared their personal experiences and insights which directly led to improvements in how determinants in the map were categorised and identified gaps in the evidence base that would not have been identified otherwise.Thank you.
Finally, we wish to thank the Campbell editorial team for their support in reviewing and producing this review.

CONTRIBUTIONS OF AUTHORS
This review was undertaken by a team with substantial expertise in systematic reviews, health behaviour and infectious diseases.
Professor Martin Dempster, Principal Investigator (PI) of the project had overall responsibility for its conduct and delivery.
vaccines and treatments are not yet established.The effectiveness of these behaviour change interventions will be determined, to some extent, by how they address the psychological and psychosocial variables that influence behaviour.To optimise public health intervention, we need to know which specific variables are most likely to influence the target behaviours, such as handwashing, in this context.Evidence gathered in the context of COVID-19 can inform who, when and under what circumstances people do or do not adopt recommended preventive behaviours.There are a number of related published and ongoing reviews on individual determinants of COVID-19 health-related behaviours but none with the broad scope of this review.Using robust search, retrieval, and methodological approaches to minimise potential sources of bias, this review examines the existing and emerging evidence on determinants of handwashing in the context of the COVID-19 pandemic.
L E 3 (Continued) , who also completed the quality appraisal assessment.Any studies identified as ineligible during data extraction stage were listed as 'excluded'.A second author checked the data extraction and risk of bias assessments on 20% of all included papers.The two people who completed the data extraction for each study discussed any discrepancies until they reach a consensus or, referred to a third author to make a final decision.In addition, the research team met on a weekly basis to discuss extraction and discrepancies, in aid coherence to the extraction protocol.Where data was not available or was missing within an included study, the research team attempted to obtain or clarify data from the relevant authors.

(
Wang et al., 2021); Apanga 2021(Apanga & Kumbeni, 2021), factory workers(Pan 2020(Pan et al., 2020), people with type 1 diabetes(Pal et al., 2020), student pharmacists(Li 2021(Li et al., 2021), visitors to a medical centre(Kebede 2020(Kebede et al., 2020), and university students(Graupensperger 2021(Graupensperger et al., 2021);Barrett 2021(Barrett & Cheung, 2021).All studies included participants over 18 years old.Reporting of age varied between studies, some providing mean age of participants, others providing percentage of age ranges and some not reporting age(Callaghan 2021(Callaghan et al., 2021); Fujii 2021).For those studies that did report on age of participants, the average age was 35.5 years.Reported outcome: Studies varied in their approaches to measuring hand washing.Measures ranged from scales (e.g.Hsing 2021) to single items (e.g., Are you regularly washing you hands with soap and water?, Pal et al., 2020).Handwashing was defined as using soap and water or using hand sanitizer within the included studies.Some studies measured adherence to specific hand washing guidance within the country of origin (e.g., Al-Shammary 2021 (Al-Shammary et al., 2021), some measured the frequency of handwashing (e.g., washed hands with soap or used hand sanitizer several times a day, Bruine de Bruin et al. 2020 (Bruine de Bruin et al., 2020), or measured handwashing following various activities (e.g.I have washed my hands every time I came into contact with objects or external environments, Trifiletti 2021 (Trifiletti et al., 2021).Determinants: There were 18 determinants analysed across the 56 studies, including worry, perceived risk, knowledge, perceived barriers, and beliefs and motivation.Multiple determinants were reported within individual studies, for exampleRui et al., 2021 reported on perceived self-efficacy, perceived risk, perceived susceptibility, and knowledge.The most commonly reported determinant was perceived susceptibility of COVID-19 (n = 25), followed by perceived severity of.Perceived effectiveness of handwashing (n = 3), fear of COVID (n = 3) and COVID-related anxiety (n = 3) were the least reported determinants.
Four studies(Barrett 2021;Dwipayanti 2021(Dwipayanti Ni Made et al., 2021); Hsing 2021; Li 2021) examined the relationship between perceived barriers and handwashing behaviour Table8.One study(Hsing 2021) recruited samples from four different F I G U R E 5 Relationship between handwashing and attitude towards handwashing.CI, confidence interval.F I G U R E 6 Relationship between handwashing and self-efficacy.CI, confidence interval.

(
Al-Shammary 2021; Jovančević & Milićević 2020; Rattay 2021 (Petra et al., 2021) which examined the role of fear on handwashing, supporting the contention that these behaviours may be driven or motivated by the level of COVID-19 related fear (Table 9).Worry or concern about COVID-19 was also consistently found to be associated with handwashing behaviours in five of the included studies (Al-Sejari 2021; Callaghan 2021; Rattay 2021; Shook 2020 F I G U R E 8 Relationship between handwashing and perceieved risk.CI, confidence interval.F I G U R E 9 Relationship between handwashing and perceived severity.CI, confidence interval.F I G U R E 10 Relationship between handwashing and perceived susceptibility.CI, confidence interval.LEONARD ET AL. differences based on whether handwashing behaviour were being carried out in or outside of work contexts.Perceived susceptibility to COVID-19 and severity of COVID-19 Findings related to the influence of perceived susceptibility on handwashing or hand sanitising behaviours were mixed across the 14 included studies (Cowling et al., 2020 (Cowling et al., 2020); Dwipayanti 2021; Fujii 2021; Hsing 2021; Lahiri 2021; Lee 2020; Lee et al. (2021); Mousavi et al. 2022 (Mousavi et al. 2022); Pan 2020; Rui et al., 2021; (Rui et al., 2021); Qian 2020 (Mengcen et al., 2020); Rattay 2021; Shook 2020; Zewude et al. 2021).

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I G U R E 11 Relationship between handwashing and social norms.CI, confidence interval.F I G U R E 12 Relationship between handwashing and knowledge of behaviour.CI, confidence interval.F I G U R E 13 Relationship between handwashing and knowledge of disease.CI, confidence interval.
factors that are most associated with handwashing and hand sanitising behaviours.The focus on only malleable factors, excluding determinants such as demographic characteristics, is important, as it provides evidence to inform the development of interventions promoting handwashing.Specifically, intervention targeted at malleable determinants of protective behaviours could be used as part of effective public health messages implemented to promote handwashing and hand sanitising behaviours in the context of potential future waves of COVID-19, and other respiratory infections with pandemic potential.A total of 56 studies were suitable for inclusion in the review, representing 199,376 participants.All the included studies were online, cross-sectional studies, with the majority being published in the United States (n = 12) or China (n = 10).Thirty-five studies were published in 2021, within the first 12 months of the COVID-19 pandemic being declared.Across all 56 included studies the most common malleable determinants of handwashing behaviours were perceived susceptibility [n = 25 studies (45%) and perceived severity (n = 21 studies (38%)].Smaller numbers of studies examined determinants such as COVID-related anxiety and perceived effectiveness of handwashing [n = 3 (5%) and 3 (5%) respectively].

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Implications for practice and policyThe findings from this review indicate that emotions towards COVID (COVID-related anxiety and worry), knowledge about COVID and perceived social norms are the determinants most associated with handwashing.While determinants like perceived severity and perceived susceptibility have little to no effect on handwashing behaviour.An understanding of how these malleable determinants impact hand washing behaviour provides evidence to inform the development of future interventions, and public health campaigns.Moreover, this evidence provides important insights regarding the determinants of handwashing for potential future waves of COVID-19, and other respiratory infections.

Dr
Sean O'Connor, Dr Rachel Leonard and Dr Jennifer Hanratty was responsible for the day-to-day operation of the review, led screening, data extraction, quality assessment and reporting.Dr Ciara Keenan acted as an information retrieval specialist, designed and conducted the searches, and contributed to screening and data extraction.Ariana Axiaq, Yuan Chi, Victoria Hawkins, Kerry Campbell, Ceri Welsh, Anna Volz and Janet Ferguson contributed to screening and data-extraction.Professor Miller acted as advisor on evidence synthesis methodology.Dr Bradley was the content expert on communicable diseases and reviewed and commented on drafts.Dr Jennifer Hanratty is a psychologist and expert in evidence synthesis.She has worked in evidence synthesis since 2012 and published reviews with Campbell, Cochrane and NIHR Health Technology Assessment among others, was editor with Campbell Education Co-ordinating group, Fellow with Campbell UK & Ireland and an invited member of the international advisory board for Evidence Synthesis Ireland.Dr Sean O'Connor is a Physiotherapist and an experienced health care researcher.He has undertaken a number of systematic reviews and studies related to behavioural interventions, including in the context of COVID-19.He has an extensive knowledge of theorybased implementation models for maximising integration of evidence into practice, systematic review methods including methodological quality/risk of bias assessment and the examination of stakeholder perspectives in healthcare delivery.Dr Rachel Leonard is a Social Worker and an experienced health care researcher.She has experience of conducting and leading on a number of systematic reviews, meta-analyses, and studies related to health interventions.Dr Ciara Keenan is a methods editor and information retrieval specialist for the Campbell collaboration.She has considerable experience conducting and leading the creation of EGMs and systematic reviews.Ariana Axiaq is a third year medical student with an interest in health promotion and extensive research experience in thematic analyses and systematic reviews.Yuan Chi is Cochrane Information Specialist, Founder CEO of Yealth Technology, core team member with Cochrane COVID-19 Attitudes r = 0.264*** 0.118, 0.399 84.743 94% 0.033 6 Self-efficacy r = 0.265*** 0.146, 0.376 48.718 90% 0.021 6 (Continues)Abbreviations: CI, confidence interval; I 2 , percentage of variability due to between-study heterogeneity; k, number of effect sizes; Q, test for heterogeneity; r, correlations; SMD, standardised mean difference; τ 2 , random effects variance component.*p < 0.05; ***p < 0.001.
These included, behaviour, cognition, demographics, disease, emotions, health status, information, intervention, knowledge and other Table 1.Each category was divided into subcategories of various determinants.As above, only malleable determinants were included in this review.Therefore, the following determinants were included:Emotions captured determinants related to feelings about the disease and 'other' emotion-related determinants for example general emotional state or mood.Cognition was broken down into six subcategories: thoughts or perceptions about the protective behaviours; about COVID-19; motivations; social cognition (e.g., perceived social norms); cognitive capacity indicating a person's ability to understand or retain information; 'other' to capture any other cognitive determinant that did not fit into the previous five subcategories.Knowledge included determinants relating to knowledge about protective behaviours, knowledge about the disease and any other types of assessed knowledge, such as knowledge of regulations or knowledge of vaccines.
Determinant categories and subcategories.
Handwashing and knowledge.